{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Model Notebook.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RNGyRx-Z97C",
        "outputId": "de79185b-9d98-4fda-ad27-72f800578bb1"
      },
      "source": [
        "cd /content/drive/MyDrive/Stock Price Prediction/Google Stock Price Prediction"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Stock Price Prediction/Google Stock Price Prediction\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yN8N3DWHwVpf"
      },
      "source": [
        "# Importing Necessary Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DIhIsJNcbzyp"
      },
      "source": [
        "import numpy as np\r\n",
        "import pandas as pd\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "from keras.models import Sequential\r\n",
        "from keras.layers import Dense,LSTM,Bidirectional,Input,Dropout"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7r2M6EwCwckk"
      },
      "source": [
        "# Reading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LD3oGLgYdlc6"
      },
      "source": [
        "data=pd.read_csv('google_dataset.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "FcA2lTdadrqw",
        "outputId": "7dac10f7-2564-41c7-8c32-a1adda31f09d"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>close</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>open</th>\n",
              "      <th>volume</th>\n",
              "      <th>adjClose</th>\n",
              "      <th>adjHigh</th>\n",
              "      <th>adjLow</th>\n",
              "      <th>adjOpen</th>\n",
              "      <th>adjVolume</th>\n",
              "      <th>divCash</th>\n",
              "      <th>splitFactor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">GOOG</th>\n",
              "      <th>2016-01-11 00:00:00+00:00</th>\n",
              "      <td>716.03</td>\n",
              "      <td>718.855</td>\n",
              "      <td>703.5400</td>\n",
              "      <td>716.61</td>\n",
              "      <td>2090621</td>\n",
              "      <td>716.03</td>\n",
              "      <td>718.855</td>\n",
              "      <td>703.5400</td>\n",
              "      <td>716.61</td>\n",
              "      <td>2090621</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-12 00:00:00+00:00</th>\n",
              "      <td>726.07</td>\n",
              "      <td>728.750</td>\n",
              "      <td>717.3165</td>\n",
              "      <td>721.68</td>\n",
              "      <td>2024509</td>\n",
              "      <td>726.07</td>\n",
              "      <td>728.750</td>\n",
              "      <td>717.3165</td>\n",
              "      <td>721.68</td>\n",
              "      <td>2024509</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-13 00:00:00+00:00</th>\n",
              "      <td>700.56</td>\n",
              "      <td>734.740</td>\n",
              "      <td>698.6100</td>\n",
              "      <td>730.85</td>\n",
              "      <td>2501730</td>\n",
              "      <td>700.56</td>\n",
              "      <td>734.740</td>\n",
              "      <td>698.6100</td>\n",
              "      <td>730.85</td>\n",
              "      <td>2501730</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-14 00:00:00+00:00</th>\n",
              "      <td>714.72</td>\n",
              "      <td>721.925</td>\n",
              "      <td>689.1000</td>\n",
              "      <td>705.38</td>\n",
              "      <td>2225768</td>\n",
              "      <td>714.72</td>\n",
              "      <td>721.925</td>\n",
              "      <td>689.1000</td>\n",
              "      <td>705.38</td>\n",
              "      <td>2225768</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-15 00:00:00+00:00</th>\n",
              "      <td>694.45</td>\n",
              "      <td>706.740</td>\n",
              "      <td>685.3700</td>\n",
              "      <td>692.29</td>\n",
              "      <td>3608109</td>\n",
              "      <td>694.45</td>\n",
              "      <td>706.740</td>\n",
              "      <td>685.3700</td>\n",
              "      <td>692.29</td>\n",
              "      <td>3608109</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                 close     high  ...  divCash  splitFactor\n",
              "GOOG 2016-01-11 00:00:00+00:00  716.03  718.855  ...      0.0          1.0\n",
              "     2016-01-12 00:00:00+00:00  726.07  728.750  ...      0.0          1.0\n",
              "     2016-01-13 00:00:00+00:00  700.56  734.740  ...      0.0          1.0\n",
              "     2016-01-14 00:00:00+00:00  714.72  721.925  ...      0.0          1.0\n",
              "     2016-01-15 00:00:00+00:00  694.45  706.740  ...      0.0          1.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ATB9RZHwgFd"
      },
      "source": [
        "# Extracting the Closing Price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QNXfLYJxdvOE"
      },
      "source": [
        "close_price=data['close']"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEEIdniagHP_",
        "outputId": "e435fc01-0aea-4c56-9125-8ea449488953"
      },
      "source": [
        "close_price"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GOOG  2016-01-11 00:00:00+00:00     716.03\n",
              "      2016-01-12 00:00:00+00:00     726.07\n",
              "      2016-01-13 00:00:00+00:00     700.56\n",
              "      2016-01-14 00:00:00+00:00     714.72\n",
              "      2016-01-15 00:00:00+00:00     694.45\n",
              "                                    ...   \n",
              "      2020-12-30 00:00:00+00:00    1739.52\n",
              "      2020-12-31 00:00:00+00:00    1751.88\n",
              "      2021-01-04 00:00:00+00:00    1728.24\n",
              "      2021-01-05 00:00:00+00:00    1740.92\n",
              "      2021-01-06 00:00:00+00:00    1735.29\n",
              "Name: close, Length: 1257, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8OIKqgPogIRm"
      },
      "source": [
        "close_price=np.array(close_price)"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GMeFBaxgOjp",
        "outputId": "1d7645e8-405f-4f61-9bca-3b776eee21ee"
      },
      "source": [
        "close_price"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 716.03,  726.07,  700.56, ..., 1728.24, 1740.92, 1735.29])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XwXHouUjgb8H",
        "outputId": "053d6b34-b0f9-40f6-97fe-652b3d4db4f3"
      },
      "source": [
        "close_price.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1257,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6n0Wt90wnG4"
      },
      "source": [
        "# Plotting the closing price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "S3aFGZ9dlhae",
        "outputId": "3c974b33-5efa-499b-b78f-e85c2529a8df"
      },
      "source": [
        "plt.plot(close_price)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f24993f0a90>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6LG+aYrcJPSWzqsHFjpIanvo4+Fz7QMVV/ikXvJuWZyTFMzQr2TcjqK8K5yNtLDAaWC0iBcBw4CsRGQwUAc6df4fbZaHKlVIqJO+2iNefNK7Na+94fT3r9lS0ed3Bmka/x8kB/faR3DXTFToc9I0x3xhjBhlj8o0x+VhdNYcbY/YBrwHft2fxHA1UGGP2AkuAuSKSbQ/gzrXLlFIqJG9XTGBgdnJ+IOwurWvzOctrA4J+gv9zx/X3oC8izwOfAhNFpFBErmrl8reA7cBW4K/ATwCMMaXAXcAX9p877TKllArJO0DbWg4cZx98e1rpgcnbUhL6V0u/zYFcY8zFbZzPdxwb4NoQ1y0CFnWwfkqpfuxvy7cDre8z6+yDj21H30V5bXPQnz4iy5fKwSvStjfsapqGQSkVUUprGjnrzx9xxZx8XvqyEIDqgF2znOIdQb89AdvZ0j9/5vBO1DQ6adBXSkWUlQWlFJXXcfebG3xl86YMDnm9s3snph1Bf7sjd35eelLQa350/FiOHZfbnupGHQ36SqmI8ssXV7coa21lrLMP3uNpPdvmK6uKeOXrPb7H4walBb1u4WmT2qpm1OrbqxCUUlHlw83FVLXSlROMxzGHv63NVH7+z699x/eceyijBrTM59PXadBXSvWqDzcXU2bPnf/Xl4XkpCbw1f+cyhMLZrbr592O1n178+rfOG8il84a1ecHbYPR7h2lVK+pa3SzYNHnTBuRxavXzqGorJZJg9PJSU3gpEmDuGDmcGaPHdDmc3i1N6++Nxtmf9R/71wp1esq662ZNKt3lwOwq7SOEyda6VdEhN+d1/Y+szVhBP3AaZr9Sf+9c6VUr6t0TJ8srWmkpLqBiYPTO/QcNY4xgKZ2du9MyAs+gNsfaNBXSvWKJreHZ1c0b8dx2p+WAZCXEXwaZSjfnz3Kt6rWm6CtNUMzkzhiVORuctLdNOgrpXrFg+9u4alPCnyP91da2S/TOtjfPj4vnbV3zCNGWqZYCJQcH8sZU4d0uK59iQZ9pVSvKDhYG7Q8I4xB1pgYITM5vs2g7zbGt79uf6VBXynVK6rqgwfotMTwNhXPaE/Q95g+n0WzLRr0leqnPtpS4jfHvaes21PBdx/5hA82FTM0M4klPz+O/ze/eQXskKyO9el7JcTGtDlP3+0xxPbDuflOOmVTqX5o6fr9/OCZldx6xiFc/a0xPfraP31+FduKawD4xdyJTByczsTB6UwdnsnaogoyksJr6cfGCGUBufK9Xv26iKyUBPu6/t3W1aCvVD+0aV8l0HIXqZ7g3PnwyPxs3/GccbnM6USSs437qgDYdbCWkQHpFX72QnP6hX7eu6PdO0p1VqPLg2llD9dI5A32OXbrtyc5f1O5aYld/vwFB2v8HgdO49xTUd/lrxlNNOgr1Qn1TW4m3Po2D767tber0m7GGJ78uACAnNReCPqOD8jUxO7vbPjp86v8Hje62rdqt6/SoK9UJ6wptDbiXryqsJdr0n5fFJT12mu7PSbkVM2uEjhOG3i/Lo8GfaVUmPZXWl0FWcnhDT72Bmfqg56cvWOM4f/+sxGwunV+eeqEHnndeVPy/B63Nz9PX6VBX6lOKKm2VpEmxsXy+uo93Lz4G8BaGXrOXz5my/6q3qxeUN46g7VYqaP2lNeRv/BN/rN2X5vXfrmzlAp7T9r1eyt5fJm15+1Dl8zg+pPHd/i1wxGYj6e9+Xn6Kg36SnWCbzGQwPXPr+L5z3exfEsxf353C6t3l/O7JZt6t4JBeNMdALjCaOl7B0qf+Gh7yGtKaxqpb3Lz3Uc+5ey/fATA+xsP+M4P6MaxhMDPscA+fFc/b+nrlE2lOqHBDiiFpc391Jc98bnvONSq09706uoi37E7jADonUe/pzz4LBi3x3D4XUt9j3faffjb7bn586bkkZ+b2uHXba/ALquGwKDfCwvSIom29JXqhPomK5d7qGmA7d3Jqbt8sOkAP31+lW/GjMdjKCqrIy/DmioZTvW8QXVvRV3Q81sOBO/SqmpwMWlwOo9dNrNb89kH9tkHbqE4Y0RWt712NNCgr1QnBLYi42KE6Y6gktHLA7yXP/kFr63eQ32TVc+i8joaXB6uPtZahesOYyaLdxwgVIN5b5APQGMM1fWuHtmxKrDPvtHVvMnKL06dwM9O6ZkB5EilQV+pTvC29L1mjcmhpsFFVooV7COle6eqwarHp9sOAnD4KOuDKZyuDo/jZ4ItSiu2xwy+M2OYr2x3aR1VDU2k9cC8/Fte+cbvsbNPf9ygNGL7+ZLcNoO+iCwSkQMistZRdp+IbBSRNSLysohkOc7dLCJbRWSTiMxzlM+3y7aKyMKuvxWlep63pZ+ZHM+/fjSboZnJlNU2+QZ428r62FP2VdSTv/BNbvr3GgZnJHHYMOu/rCeMoO/sMw+8v/omt2/Nwi/nTSQxzgoxX+0qY8PeKoZkJYd7C+1WXttcJ2MM+xzfPEYFpGfoj9rT0n8KmB9QthQ41BgzFdgM3AwgIpOBi4Ap9s88LCKxIhIL/AU4DZgMXGxfq1RUa2hyM3lIBl/eegoz83PIz02lpLrBN4PEGYB6k7eFD1bg86YXDqel75zm+cm2g34bk//xnS18tr0UgGFZybz7y+MB+Pk/v8btMZzZjRuYvHbdnBZlX+0qY09FPf9z5mSW33QiU4ZmdtvrR4s2g74xZhlQGlD2X2OMN6HFZ8Bw+/gc4AVjTIMxZgewFTjK/rPVGLPdGNMIvGBfq1TUqqhtYndpHckJscTZA5OTh2T4zk/MS2/REn5n/X6OuuedHu/2Wbal2Hc8PDuFmBhBJLzFWc5hgJ88+xXX/H0lxVVWl463u2tinrXP7cB0/9w604Z33yDq4CDbLHrHF44dl8uIHG3lQ9f06V8JvG0fDwN2O84V2mWhylsQkWtEZKWIrCwuLg52iVIR4bJFK9i0v8qvn3rWGGvv1RMmDuTs6UNpcHl8gbCu0c3Vz6zkQFUDu0uDz3zpKsYYPtlW4nv88dbmlv5ce4VqXIyEFfQDF3Qt31LCkfe8w+c7Sqm2Nyl/7gezAGvRmlN35toJ1ldfWWfVJzOKVkx3t069AyJyC+ACnu2a6oAx5nHgcYCZM2f27wm1KmK53B5f3p1KR6s9JSGOgnvPAGDxV1bf9p7yOoZlJ/Psip2+6wKnEXa1zfurueSvK/zKjhk7gG9PH8aph1hBP0aEhz/YxhlTh3So2yPUOMAFj33qOx7QDdkz2xIs6BeVW2sEemLWULQIu6UvIpcDZwKXmuYh/CJghOOy4XZZqHKlotLS9ft9x6E2/RhpdyfsKq3lh3//krvf3OA75+wH7w7B5tAPzUrmgiNH+PaI9Q5C/+qlNR167nDz9XR3rp1gQf+VVXsASEmIbXGuvwor6IvIfOAm4GxjjDNl3mvARSKSKCKjgfHA58AXwHgRGS0iCViDva91rupKNXN7DB9sOtDtwdTLuxhr+ogsbj59UtBrvEH/qqdX8sEm/67KuiZXsB/pMqV2vvxpwzOZOtxqxYfq4jhY3cC5D3/Mh5vb150aTr4eoNtz7QQL+pnJ8QxKT0T6+RaJTu2Zsvk88CkwUUQKReQq4CEgHVgqIl+LyKMAxph1wIvAeuA/wLXGGLc96HsdsATYALxoX6tUl3hg6WYuf/ILfv7PVW1fHIbFXxWSv/BN34YctXbf9Us/ms2kwRlBf8Y7iOlsGXsDb11j93bveGcNPX3lURxi1y/UN5IDVQ2s2lXOr15a3a7n7ug0z6PH5HD8hIEd+plwBAv6sTHCocN0xo5Tmx1dxpiLgxQ/0cr19wD3BCl/C3irQ7VTqh0aXG4eet/axGTJuv0cqKxnUMBMjs+2H2TS4HTfPqkd5Q2If3p3CzefdgjVjS4S4mJaTScQrHU5b0oeL64spKahe1v63g+nlIQ4xuelATBmYOv5bhLamRqhoy39F66Z3aHrwxUXsPety+2hweXu94uxAumKXBX1Dlb77/O6r9I/DcDeijouevwz/ufV8L9cnmwPfnrTBNc2uNu1uvSsaUP9Hv9q3kRiBArLu2/2zvItxfz+v5sBSIiL4Yo5o3liwcwWc+SnDfdvAbc3OLbWpz9+UBofLzypgzXuGs7qv/p1EeNueZvN+6t9axKURYO+inreoH+GHdQCU+lu3l8NwO7S8Hds2lFiZYhcU1iBy+3h75/tbOMnLA9eNJ1LZo0E4MZ5ExmUnsSw7GQKSmra+MnwPfOpf91iY4STD8lr8c3jH1fP4rmrZ/ke762oa7XrxuMxPP1JgW9OvtfGu+Zz0ZHWPI0Fx+QzrAdW3QbjvL/fvL7ed6wtfX86j0lFvZIaKwhNGZrBm2v2tgj63sHdr3eXU1zV0GLBUFsaXG62HrA+OLaXVLPJ3hglox3TAEWEO86awuQhGVxoB8b8Aam+D5GuUFRex4DUBJLirRkq7Z2pkp4UzzHjcn2Pm9yGl77czYVHjgx6/UdbS7j9Nf9vSyKQFB/LuEFWF9LYgWnh3EKXc4Z5ben705a+inoldstzaKbVwvTOgf9oSwnnPPSR3zz6dXsqOvTcFXVN7LLzwWelxFPf5PHlkf/DBdPb9RwJcTF87+hRvv7/0bmpFJTUBE1W1lFNbg9z7n3PbxA2sLurI95uZTesv7zvv/n7B786gQ13WhlarpwzmlevncPssQPCfu2u5PxSExujYc5JW/oq6pXYQW5IpjV42+ibf76afZX1HHuwuVV9oLKh5RO0Yt4Dy3xjBCOyUyivrWCn/XzenPQdNWpAKlUNLspqm8jp5A5SZbXWvb+7oXlXKm8+++tPGtfh59tid4UFKq1pZMUOv2wsJMXH+r5dxMQI0yIoT32J44NPW/r+9CNQRTXnZtvemTnefOrezTRKa5pb+gdr2t8KdnuM36Cwd+qfdyeoAanhBX1vt1BXzODxzsdPim/+r1xW28QPjxvDL+dO7PDzFZXXURRkkPk3r7ccBI+Wqe+xsVFS0R6iQV9FtWLHJt8JdhrfRrfVh+8N+sVVzYG7rqn9i7dqGpuDckpCLLNGW3l1vIO4yWGu8myuZ+fn6nuDfkJcDHWNbuqb3DS6PGSmdDzXzO/PnwZYaSO8ahpcuD2GV7+2VrbeOM/6ILn2xLEM6uDYSG/Rlr4/Dfoqqr3wuZXH78nLjyTebtE1ubwtfevvwrI6UhNiSY6PbbHpSWuq65uD/hGjsskLksUxHN758Cf/4UPecaRzCEehnbhtf2UDh9z2Hx5fZm1W3pEEY94W+6TBVmbMFdut5Gxuj2HK7Uu40TFecPa0oRTcewY3zpsUNatcu3LQvC/QoK+iWqWduvj4CQN9LegGuwXtbdVvOVDNgLREkhNifYuW2sPb/TJ3ch7/+53DmDw0+MrbjnIu6Lp/6eZOPddN//bPm+N9vo4kUHvnF8dz33lTGWyPiTz1SQHQ3OJfvKo5TVZvTcfsjOVbStq+qB/RgVwVtQrLavnbRzvITUsgJkZ8Legml4c1heW+69wew4C0BNwe06H0B1V20L941kiGZ6d0yWwbgPi45qAf6puHx2N4ceVuvj1jmG+wtL1y0xL89ulty9iBab6plgPTE32BPbCF/NAlM3zJ2qKJztP3py19FbUe/XAb0Lxln7OvfHuxf8AakZ1CUnxMm4nOquqbeH31Howxvpa+d+VtV3VnONMdbC+p4R9BFnq9vmYPCxd/wyMfbAv6HC63hya3h/wg2/9dMmtU2HWbPWaAb0bQwoBvEYePzA77eXvKyZMGtShLitMw56QtfRW1CkqsWTQv/tDK7RLvaOl/U1RBYlwMMSLUNbnJH5BCSXUDX+8qxxgTMoDPfWAZeyvqmTw0o0XQB1jy8+P4ZFsJR+bnhF3vhDj/1771lbVcOmskm/dXM9HuV/e2sl2e4N9Mzn34E7YVVwftuw9Mr9ARKQnN4x5VAbOLBqR1bnppT3AOjh82LJNviioYb+/ipSwa9FXUKqlu4NTJecywW6Bx9haAjW4Pq3aVMXV4Jl8UlAHW3Pjc9ERue3UdeyrqfV0YdY1udpXW+oKtd3u90ppGqupbBv2Jg9N914YrWJK2Zz7dye2vreOZK4/iuAkD2WWnjAg1ePxNkbXIrLbRzewxA9hVWvITSVQAABq0SURBVMtN8yeyt6KeEye2bO12pG5NbsOBqnrf/YO1MC1wF6xI5FyN/Z3Dh/Hr0w/p9PvV1+j3HhWVXG4Pe8rr/FIqiAjxMTHUN7nZUVLj9589PzeV4dlWoHfmjvnFi18z74/LqG10+aZ4gtXS9rb0u3qLv4Qg3Q1r7SD+1S7rQ2rpOmtWT2DmSMCvnmDNLPp44UmcM30YPzp+bKf63eNihSa3h//3L6trx9sVFSotc6RxbvQeFyPMHjug0wvg+hoN+iqqbCuu5p31+9laXE1lvYsj8/37mRvdHv66fAfldU3kOBZP5Q9I8Q2IOjdaWWZvHFJc1eDLQQ9w07/WcIedtKs9OXY6wvvNwbmJ+ucF1mpX77x7b9dKo8t/oHd/ZT0vrSz0K+vKD6X42Biq6l28b2/6ct/5UwHYXRZ+srqedP8F03zHutdqcBr0VVQ575FPuPqZley1898MyQw+hdAYyHEsUMpJTSAlwQqOzsHcGvsDYH9lQ8gVsnHtzDPfXsOzU7jn3EO597uH+cq8q3xrGvyDfOACrlm/fZdfv/yNX1laYtd1u8Q7Vq9mJsdzip1S+upjR3fZa3SnUQNSWTDbGsgOd1vHvk779FVUKbNb4+9utLo/Wvvqnp2aQGpCLDWNbkSEZF9L3wqkzimY3xRV+Fbc9oRLQ8yw+aKg1JfgDeC3b23kgpkjyEpJ4GB18LxBri4Mbs7upGevnkVqYhyrb5/brr0DIkWSvVK6toe2zow22tJXUWWwPbD5j892AZDdyk5YWSkJfLzwJFbeegrQnHLYu0DL2Yo+UFXva+nfcdZkjurE7JzO2FVay3H3ve9XNv3OpXg8pkUQu/bEsUDzB2FXcI43eD9QM5Pjo2que1ayVW/vVF7lT4O+imgNLjcPLN1MtR2QGwL6uLNbyTGTk5JAVkoCuWlW3763T//jrdYKzXrHQq1Gl4f3NlmZKqeNyOLFH83me0eP5OFLD++6m+mEmkZXi66ea44by+mHDeayo8Oflx/ImacmWgdAR+daaxei6dtJT9KgryLa29/s40/vbuH+/26mweWmrLaJ4xybbAf2t586Oc93nJfpnxDM29J/xU4e5ky+1uDysH5PJQBjcq3VqXd/+zBOP8x/i8HeUlnvajHmkJkcz8OXHtHhTWFas+VAc2rljq4EjhTzpgzmzxfP4EfHj+3tqkQkDfoqIpXXNmKM8U1P3FNex3t2zvgz7UA8OrflRt/nTG/ek3ZQuv8c98Ag5gz6RWV17KuoZ+7kvLAyVIbrb9+f2a7rVu8u5+yHPu7m2vjPbIpWIsJZ04YGnRqrdCBXRaBdB61+7XvOPdS3SCklMZZbXlkLwOGjsth41/ygA3XeBUTeDVWcAvulncnXPrSnbnYkZ01XmNrK6tnDR2bx1S4rh9DNi5tn7Nx+1mQmDe6a5G+BvFNFZ4yMnA1RVNfSj0IVceb+8UMAbnl5LY99aKUK3nagmtKaRq6Yk8+4QekkxccG7XNOtFt3beW6/8dnOznjwY9alMf3cOvQ2Rr900X+2y/+at5EUu37cH4rmZCX3m3bEv7o+DEkx8fy5OVHdsvzq96nQV9FlIKSGuqbWuabWV1orVg9/4gRrf68N+jHtJEc7Vb7W0Og+h7u3nAG/XOmD+MXp07wPT5mbC5v/+w4wD+9QOCK3K50zNhcNtw137cLmep7NOiriGGM4aw/W63vUPvPtpXPPcEX9MOrwwlBsjR2J2+aA+9nVEnAXPyM5JY9sN7NYZQKR5tBX0QWicgBEVnrKMsRkaUissX+O9suFxF5UES2isgaETnc8TML7Ou3iMiC7rkdFc1Kaxp9fcqf3Xwylx+TD8AJE5tn6wQLgk7eZGahWvrnHzHc7/H9F0zjvvOsVAPDspI5e9rQYD/WbeJiY/jFqRN44/pjAfjh8WNZMHsUm+6eDwSfdpiTGh15cFRkak9L/ylgfkDZQuBdY8x44F37McBpwHj7zzXAI2B9SAC3A7OAo4DbvR8USnk12F0Y1544FhHhjrOnUHDvGVwxpzkFQHtz2ocK+ocM8R8APWf6MM6fOYKNd83nvV8dH2bNO+enJ4/37XQ1LCuZ35xzqG9AOi42hptPm+S79vkfHM0Ro3pn4ZjqG9oM+saYZUBpQPE5wNP28dPAtx3lzxjLZ0CWiAwB5gFLjTGlxpgyYCktP0hUP+cN+uMGpfmVe7Njtoc330qQ5JQAZDtayXMn5/lm9CTFx0Zs6mDnjKLuGsBV/Ue4UzbzjDF77eN9gHdFzDBgt+O6QrssVHkLInIN1rcERo4cGWb1VLSpqm/ybUQeGHy9/fjHtCPgTRyczrThmdx21pSg58+ZNowhmcnMGJkVsUE+UFendlb9W6f/NRljjIh02ciSMeZx4HGAmTNn6ohVP/D//rWGf65sbhMkBkybTIqP5c2fHkv+gJaLsQIlxcfy6nXHhjwfEyMcPSa6WsspbUw/Vaojwp29s9/utsH++4BdXgQ459QNt8tClaso8t7G/Vz//Cr2V9Z32XO6PcYv4EPwTUamDM3sty3e/nrfqnuEG/RfA7wzcBYArzrKv2/P4jkaqLC7gZYAc0Uk2x7AnWuXqShRWtPIlU+t5PXVe7j/v5uDXnPv2xs59PaOva3ezc0HOBZaRUu3S0/RoK+6UnumbD4PfApMFJFCEbkKuBc4VUS2AKfYjwHeArYDW4G/Aj8BMMaUAncBX9h/7rTLVBQwxnD4XUt9j7OCTBl0uT08+uE2qhtcfnnq23Lfkk0AnO3ImRPYvdPfeVflHtWD+f5V39VmE8IYc3GIUycHudYA14Z4nkXAog7VTkWEtUWVfo+Ngcr6Jv67bj/n2fPevZkrwUoZ4N2lqr0uO3oUT35cALScvdPfiQjr75wXVTntVeTS742qTTtLa/we1za6mP3bd6lpdDN1eCYT8tLZtK/5g6GmoX1B//Md1pe9X82dwJiBaTx48Qxmjc7R7owgOvohqlQo+j1atelApZUa4NObTyI3LZF/fLbLt7fswWorBfJfl+/wXe/MXhmKMYYLHvvUPrbKzp42lLyMltkxlVJdR4O+atO24mrSE+PIS09qkRvmQFU9eyv8Z/MUVwXfyzXUNdlRukOTUtFIg75q1aKPdvDsil0cOiyTmCB9yrsO1vJFgdVN89hlRwDwN0er38sY4zfA++XOMgBunDeRi4/SRXhK9RQN+iooj8ew9UA1d76xHoCpI6zcMH+5xH/P2D8s3cy6PZUkxMVw6iHWwuz/rNvXYgbPL15czeTblvDexv0UVzVQWFYHwGWzR+kApVI9SEeHVFCnP7icjfuqfI+nDrPyv5wxdQhH5p/M/soGznrISoNcWFbL4Iwkv28Cjy/bzlXHjvbtYfvyKmst3pVPrfR7nXQdtFWqR+n/ONVCg8vtF/ABvjUh13c8KCOJQY4B12WbS3xzyB+6ZAbXPbeK/317I8+u2MWu0loyk0OnAm5v1kylVNfQ7h3Vgndevl8e+6SWgdsb6KsbXJxoX3vm1KF8Z4aVS8+7v21FXRMA01rZD1Yp1TO0pa9aWLXLGmT93Xen8v6mA+yrCD4b5+xpQ31z7YfnpPjK7zt/GjWNLpas2+93/cPfO4KisjrfVM3FPzmmO6qvlGqFBn3Vwqpd5QzLSmZQRhIXHhl6Zo0z6+WI7OagHxsjVNX7z9U/e9pQhmYmMSQjiRvnTeQ7h1spjpVSPUuDvmph475KDh2W0eZ1owc6gn6OfwD/2cnj2V1WS3xsDCNzUnjw4hmAtRfstSeO69oKK6XaTYO+aqG6wUVWctsLpobYg7njBqW1yIw5a8wAlt90UrfUTykVPg36qoXaRjfJ7di4IyZGeOP6YxmSqakTlIoWGvSVH2MMtY3udu/WdOgwnZGjVDTRKZvKT6Pbg9tjNNOlUn2UBn3lxzsFU/dlVapv0qCv/Cz+ykqXcNyEgW1cqZSKRhr0lZ+CgzXMHjOAsQN19yql+iIN+v1UVX0TDS53i/KS6gbyMhJ7oUZKqZ6gQb8fqm10Mf3Opfz4H1+1OFde00RWim5qolRfpUG/n3G5PVzw2Ke4PYb3Nh7wO1fb6KKqwUW2Bn2l+iwN+n1QSXUDG/ZWtig/UFnPq1/vYW1RJTEC8bFCaU0jFz72KduKq1n4728AmDEyq6errJTqIToZuw/64d+/5MudZXxzx1zSk+KpqGviN6+tY7G9kUlWSjw/+NYY7luyiXMf/pidB2u54skv2FVay7CsZJ25o1Qfpi39Psi7/+z6PVZr/8UvdvsCPsCph+QxeYiVUG3nQSvnvTf3/c2nT+rJqiqlepgG/T7G42nem7bgYA1lNY3c89YG4mOtHaqyU+K59YzJzB47wHfdOdOH+o7HDdKpmkr1ZZ3q3hGRG4CrAQN8A1wBDAFeAAYAXwKXGWMaRSQReAY4AjgIXGiMKejM6/dlyzYXMzA9kUOGtJ3i2GlvZb3vuKy2iRe+2A3AjfMmcsbUoeSmJbTIiPnr0w/hjrOmUFLdwPi89M5XXikVscIO+iIyDPgpMNkYUyciLwIXAacDDxhjXhCRR4GrgEfsv8uMMeNE5CLg/4ALO30HfdT3F30OQMG9Z7Tr+oq6Jq599is+2lriK9teXM3yLSUclZ/DNceNbfEzf7xwOkXldeTZKZKzU3XWjlJ9XWe7d+KAZBGJA1KAvcBJwL/s808D37aPz7EfY58/WXRX7KC8ffFgZb1sjyeWb/cL+AAvrixkb0U9N86fGPRnvj1jmG5oolQ/E3bQN8YUAb8HdmEF+wqs7pxyY4x3r7xCYJh9PAzYbf+sy75+AAFE5BoRWSkiK4uLi8OtXlQ7/cHlvuNlW0qCXrOvop4z/7ycw+5Ywq6DtewuqwNg3pQ8jpswkJH2nrWTh2RwZH5O91daKRUVOtO9k43Veh8NlAMvAfM7WyFjzOPA4wAzZ85sXzM3ytU1ukmKj8H7xScuRnDZA7LPrdjJh5uKufbEsQxIa06PcM9bG1hbZH0jOO6+9xmQmsBRo3N47LKZALz6dRGPfridp648sofvRikVyTozkHsKsMMYUwwgIouBOUCWiMTZrfnhgHeuYBEwAii0u4MysQZ0+7ynPylgdWE5PzxuLBMH+w+U7i6t5Vu/e5+j8nP4/fnTOO6+9wG4cs5oNu6rZMm6/YDVZ/+HC6b5fu7zHdavLiE2hka3h4M1jZzlGPQ9Z/owzpk+DKWUcupMn/4u4GgRSbH75k8G1gPvA+fZ1ywAXrWPX7MfY59/z7S3wzqKbT1Qxe2vrWPxV0XM++MythVXU9fo5rQ/Lecv72/1rZz9vKDUF/ABctMTmDq8eWVsvSM5msvt4UBVA9edOI7N95zmKz9p0qAeuCOlVDQLu6VvjFkhIv8CvgJcwCqsbpk3gRdE5G677An7R54A/i4iW4FSrJk+fVp9k5tT7l8GwAkTB/LBpmIu/esKZuZns2FvJRv2VjJlaMspmWNyU5k/ZTAFB2t49EOrrKbBxVe7yhiUnkhRWR3GwIicZMBKp9DkNozP0zn2SqnWdWqevjHmduD2gOLtwFFBrq0Hzu/M64Wrye1hX0U9I+zBzfZ465u95GUkccSo7LBf15nQ7OFLD+f5z3dz1xvreWPNXl/5OnumTm5aApV1Lh66ZAZzpwwGoKahuXX/waZiPtjkP7B9+EirbredOZn/eXUduWmaElkp1bp+kXvn14u/4aUvC1n3m3nt2vvV4zH85Fkr7fCWe04jPja8XrAie0bN6tvmkpIQx1XHjmbykAz+9+0NrCms8F3381PG8/NTJrT4+TEDU1t9fu+H2GWz87lsdn5YdVRK9S/9Ig3DS18WAlDb2HLTkGB2HKzxHT/24bawX7eyvgkRSE9q/qCZPXYAr113LA9ePAOAacMzgwZ8gNTEOG6cF3yOPUBSvO5jq5TqmH7R0veqbwod9D0ew1G/fYe0xDjOntaci+ZgTWPYr1dV7yI9MY6YmJZr0M6eNpQzDhtCW6vTrj1xHA1Nbh58bys/PXk8cTHC/Us3c94Rw8Oul1Kq/+oXLX2vUEG/psHFoo93UFLdSMHBWh58b6tvcdOQzKSwXuvNNXt56pMCMlPiQ14TGyNBPxACebuX3B4Pc8ZZ69kuPHJEWPVSSvVvfb6l75wVWhck6Hs8hu88/Amb9lf5lQ/JTGJXaS31TZ4WP1NV38T+ynrGDQqenGzrgWqufc4aE5g9psWi4w6LtTNkujyGI0bldGqcQSnVv/X5yFFS3dw9EyyAv/J1kV/An2hnmfzu4cOJi5Gg3w6ue24Vp9y/jPV7Kv1SGXu9+rW1Hi0lIZbrTxrf6Xs4dlwuAMfbm5towFdKhavPtvRf+HwXQ7KS+c/afb6y2kZXi+t+8eJqAP55zdHsLK3lgpkjaHR5SIiL4ZZXvqGkugGA8tpGUhPjaHJ7+HCzNXXy9AeXc/kx+dxx9hS/59xeUsOY3FTe+9UJXXIvU4dnse23pxPbjq4gpZRqTZ8N+gsXf9OizDnv/fMdpb4PgQl5acwaM4BZdldMQpzVkm5yG15cWci935nK9DuXcta0ocwMmLf/1CcFXHXsaN/0yXc37OfNNXsZ38WbkWjAV0p1hT4Z9Kvqm4KWV9Q1l1/w2Ke+48uPGd3q8+2zNyZ5ffUeXl+9h+T4WCYOTufr3eUA3L90Mw9cOB2Aq55eCTQvnFJKqUjSJzuHPS277gEr6P/pnS08/UmBX7k3nUGgm0+z9ot92bG/LFgDwn++eAZ/vngGkwans/VAte9cXkYigzOSuPe7h4V/A0op1U36ZEs/1DTJ3WW1PLdiV4vyEdnB0zNMtvPi/G35dtIT46hqaB4TGJGTwoicFJZvKebFlYU8//kuzpw6hP2VDdxwygR0fxilVCTqky19gEHp/nloYgQKSmqCXjs0K3hLf9oIK8tlWW0T+bmp/OF8K7XxrWcc4rsmO8XaYvDmxd9w2B3/BUJ/c1BKqd7WZ4P+K9fOAWD6iCyW33Qi+bmp7HAE/Ul2XvtB6Ym+gdtA6Y48PeMGpfHdI4bz+a9P5upvjfGVBxtg1f58pVSk6pPdO2C13r+45RRy0xIQETKT49lebAX9/95wHBPy0qmsb6KulXw8IsKVc0azYsdB37TMQRn+K3TPnzmCV7/eQ1G5lVzt5Z8cQ35u64nSlFKqt/TZoA8w0NHFk5Xc3M+fP8AKyhlJ8WQkhU6TAHDbWZNbPT86N5W7zz2UK578AoDDhmWGW12llOp2fbZ7J9DNpx9Ccnwsz1x5VMjunHB5u4FiBOJ0taxSKoL16Za+04S8dDbc1el924MaM9BaiJWZ3Pq3BqWU6m39Juh3p5zUBG4+bRJz7Bw5SikVqTTod5EfHj+2t6uglFJt0g5opZTqRzToK6VUP6JBXyml+hEN+kop1Y9o0FdKqX5Eg75SSvUjGvSVUqof0aCvlFL9iBhjersOIYlIMbCzE0+RC5R0UXV6Q7TXH6L/HqK9/hD996D177hRxpiBwU5EdNDvLBFZaYyZ2dv1CFe01x+i/x6ivf4Q/feg9e9a2r2jlFL9iAZ9pZTqR/p60H+8tyvQSdFef4j+e4j2+kP034PWvwv16T59pZRS/vp6S18ppZSDBn2llOpH+mTQF5H5IrJJRLaKyMLerk8wIjJCRN4XkfUisk5EfmaX54jIUhHZYv+dbZeLiDxo39MaETm8d++gmYjEisgqEXnDfjxaRFbYdf2niCTY5Yn24632+fzerLddpywR+ZeIbBSRDSIyO9reAxG5wf43tFZEnheRpEh/D0RkkYgcEJG1jrIO/95FZIF9/RYRWdDL9b/P/ne0RkReFpEsx7mb7fpvEpF5jvKej1XGmD71B4gFtgFjgARgNTC5t+sVpJ5DgMPt43RgMzAZ+B2w0C5fCPyffXw68DYgwNHAit6+B8e9/AJ4DnjDfvwicJF9/CjwY/v4J8Cj9vFFwD8joO5PA1fbxwlAVjS9B8AwYAeQ7PjdXx7p7wFwHHA4sNZR1qHfO5ADbLf/zraPs3ux/nOBOPv4/xz1n2zHoURgtB2fYnsrVvXqP9huejNmA0scj28Gbu7terWj3q8CpwKbgCF22RBgk338GHCx43rfdb1c7+HAu8BJwBv2f8wSxz9+3/sBLAFm28dx9nXSi3XPtAOmBJRHzXtgB/3dduCLs9+DedHwHgD5AUGzQ7934GLgMUe533U9Xf+Ac+cCz9rHfjHI+x70Vqzqi9073v8EXoV2WcSyv2LPAFYAecaYvfapfUCefRyp9/VH4CbAYz8eAJQbY1z2Y2c9ffdgn6+wr+8to4Fi4Em7e+pvIpJKFL0Hxpgi4PfALmAv1u/0S6LnPXDq6O894t4Phyuxvp1AhNW/Lwb9qCIiacC/gZ8bYyqd54z18R+xc2pF5EzggDHmy96uS5jisL6iP2KMmQHUYHUr+ETBe5ANnIP1ATYUSAXm92qlukCk/95bIyK3AC7g2d6uSzB9MegXASMcj4fbZRFHROKxAv6zxpjFdvF+ERlinx8CHLDLI/G+5gBni0gB8AJWF8+fgCwRibOvcdbTdw/2+UzgYE9WOEAhUGiMWWE//hfWh0A0vQenADuMMcXGmCZgMdb7Ei3vgVNHf+8R936IyOXAmcCl9gcXRFj9+2LQ/wIYb89eSMAarHqtl+vUgogI8ASwwRhzv+PUa4B3FsICrL5+b/n37ZkMRwMVjq/CvcIYc7MxZrgxJh/r9/yeMeZS4H3gPPuywHvw3tt59vW91pozxuwDdovIRLvoZGA9UfQeYHXrHC0iKfa/Ke89RMV7EKCjv/clwFwRyba/8cy1y3qFiMzH6uo82xhT6zj1GnCRPXNqNDAe+JzeilU9NejRk3+wRvs3Y42M39Lb9QlRx2Oxvr6uAb62/5yO1b/6LrAFeAfIsa8X4C/2PX0DzOztewi4nxNonr0zBusf9VbgJSDRLk+yH2+1z4+JgHpPB1ba78MrWLNAouo9AH4DbATWAn/HmiUS0e8B8DzWGEQT1jeuq8L5vWP1nW+1/1zRy/XfitVH7/3//Kjj+lvs+m8CTnOU93is0jQMSinVj/TF7h2llFIhaNBXSql+RIO+Ukr1Ixr0lVKqH9Ggr5RS/YgGfaWU6kc06CulVD/y/wGMEWVrA4/RsgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ch16lR8Cwsir"
      },
      "source": [
        "# Applying MinMax Scaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MXwiXeK3IXfh"
      },
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k-KDDSYjIvwy"
      },
      "source": [
        "minmax=MinMaxScaler()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jlAKTYdeI0GA"
      },
      "source": [
        "close_price=minmax.fit_transform(close_price.reshape(-1,1))"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wtFy1qPCI_rD",
        "outputId": "a9c01f18-9a37-4730-f059-5a5fe1161d96"
      },
      "source": [
        "minmax.data_min_"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([668.26])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kL-6IYdJdhu",
        "outputId": "8f40f5ff-f147-46d4-dd96-8ade174a5217"
      },
      "source": [
        "minmax.data_max_"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1827.99])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pak8Sr7twwyC"
      },
      "source": [
        "# Converting the data into time stamps of 100 days"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUYOL7E0gPOl"
      },
      "source": [
        "sample=[]\r\n",
        "data_stamps=[]\r\n",
        "price=[]\r\n",
        "\r\n",
        "\r\n",
        "for i in range(1157):\r\n",
        "  sample=[]\r\n",
        "  for j in range(i,100+i):\r\n",
        "    sample.append(close_price[j])\r\n",
        "  price.append(close_price[100+i])\r\n",
        "  data_stamps.append(sample)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4abBKl7pkUu7"
      },
      "source": [
        "data_stamps=np.array(data_stamps)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWXctcFumoPX",
        "outputId": "0a7c7de2-dbb1-4d71-cef0-4b8b9269d55c"
      },
      "source": [
        "data_stamps.shape"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1157, 100, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1rII_5zSsoR5"
      },
      "source": [
        "price=np.array(price)"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MzosglwIssdE",
        "outputId": "a13b4eff-0dc8-4041-8c02-43d4b68d2fdd"
      },
      "source": [
        "price.shape"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1157, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V3LLGZViw4EW"
      },
      "source": [
        "# Splitting the datastamps into training and testing samples"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o2JoZMuSpUBD"
      },
      "source": [
        "x_train=data_stamps[:int(0.7*len(list(data_stamps)))]\r\n",
        "y_train=price[:int(0.7*len(list(data_stamps)))]\r\n",
        "\r\n",
        "x_test=data_stamps[int(0.7*len(list(data_stamps))):]\r\n",
        "y_test=price[int(0.7*len(list(data_stamps))):]\r\n"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "npxZ_Yiew-0n"
      },
      "source": [
        "# Defining a nueral network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Se7CghEiosgv"
      },
      "source": [
        "model=Sequential()\r\n",
        "model.add(Bidirectional(LSTM(100,return_sequences=True),input_shape=(100,1)))\r\n",
        "model.add(Bidirectional(LSTM(100)))\r\n",
        "model.add(Dense(64,activation='relu'))\r\n",
        "model.add(Dense(1,activation='linear'))\r\n"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8bSxERhPqbuO",
        "outputId": "7fade20c-e167-4beb-b73e-acbab0b81de2"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "bidirectional (Bidirectional (None, 100, 200)          81600     \n",
            "_________________________________________________________________\n",
            "bidirectional_1 (Bidirection (None, 200)               240800    \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 64)                12864     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 65        \n",
            "=================================================================\n",
            "Total params: 335,329\n",
            "Trainable params: 335,329\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opq6AdbPoqvk"
      },
      "source": [
        "model.compile(optimizer='Adam',loss='mse')"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffEAVYk2xEC9"
      },
      "source": [
        "# Training a model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpolnA2kr5EH",
        "outputId": "b87abedc-c9af-4362-9338-855c63d51852"
      },
      "source": [
        "model.fit(x=x_train,y=y_train,epochs=50)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "26/26 [==============================] - 12s 25ms/step - loss: 0.0250\n",
            "Epoch 2/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 0.0016\n",
            "Epoch 3/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 9.6277e-04\n",
            "Epoch 4/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 7.1448e-04\n",
            "Epoch 5/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 5.2108e-04\n",
            "Epoch 6/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 6.0279e-04\n",
            "Epoch 7/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.7475e-04\n",
            "Epoch 8/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 6.4347e-04\n",
            "Epoch 9/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 5.5657e-04\n",
            "Epoch 10/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 5.9857e-04\n",
            "Epoch 11/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 4.7923e-04\n",
            "Epoch 12/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.6964e-04\n",
            "Epoch 13/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.8566e-04\n",
            "Epoch 14/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 4.4481e-04\n",
            "Epoch 15/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.3463e-04\n",
            "Epoch 16/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 5.0256e-04\n",
            "Epoch 17/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.1896e-04\n",
            "Epoch 18/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.9699e-04\n",
            "Epoch 19/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 5.4902e-04\n",
            "Epoch 20/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.7809e-04\n",
            "Epoch 21/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.4450e-04\n",
            "Epoch 22/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 3.2939e-04\n",
            "Epoch 23/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.2381e-04\n",
            "Epoch 24/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.1783e-04\n",
            "Epoch 25/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.9106e-04\n",
            "Epoch 26/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 4.3352e-04\n",
            "Epoch 27/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.4535e-04\n",
            "Epoch 28/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.8489e-04\n",
            "Epoch 29/50\n",
            "26/26 [==============================] - 1s 23ms/step - loss: 3.4440e-04\n",
            "Epoch 30/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.9788e-04\n",
            "Epoch 31/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.3874e-04\n",
            "Epoch 32/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.1048e-04\n",
            "Epoch 33/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.7900e-04\n",
            "Epoch 34/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.7123e-04\n",
            "Epoch 35/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.6820e-04\n",
            "Epoch 36/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.4626e-04\n",
            "Epoch 37/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.8197e-04\n",
            "Epoch 38/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.6126e-04\n",
            "Epoch 39/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.5234e-04\n",
            "Epoch 40/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.2373e-04\n",
            "Epoch 41/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.6939e-04\n",
            "Epoch 42/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.3586e-04\n",
            "Epoch 43/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 3.0846e-04\n",
            "Epoch 44/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.6801e-04\n",
            "Epoch 45/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.4328e-04\n",
            "Epoch 46/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.2027e-04\n",
            "Epoch 47/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.8362e-04\n",
            "Epoch 48/50\n",
            "26/26 [==============================] - 1s 25ms/step - loss: 2.9308e-04\n",
            "Epoch 49/50\n",
            "26/26 [==============================] - 1s 25ms/step - loss: 2.5322e-04\n",
            "Epoch 50/50\n",
            "26/26 [==============================] - 1s 24ms/step - loss: 2.2908e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f24227a70f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJJERmYvqokX",
        "outputId": "6727394d-c429-498f-e0a1-6842e45aa5d2"
      },
      "source": [
        "model.evaluate(x_test,y_test)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11/11 [==============================] - 2s 10ms/step - loss: 0.0022\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0022354447282850742"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BTIhVTfvxTTX"
      },
      "source": [
        "# Predicting the test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QGgw3vCPqrz9"
      },
      "source": [
        "y_pred=model.predict(x_test)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wTvNqk2Sq0hP",
        "outputId": "4008507d-ec34-48f2-c910-ce8683843539"
      },
      "source": [
        "y_pred"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h29dCFyNxaGv"
      },
      "source": [
        "# Plotting closing prices of testing vs predicted data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "XrROPgSsq1yd",
        "outputId": "97eeb308-2482-4da1-b969-58681423e8ae"
      },
      "source": [
        "plt.plot(y_pred)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f241e4d4cf8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "PEQn6XdGq96U",
        "outputId": "47683f3a-ad10-4ac9-f44e-cee02db70e7b"
      },
      "source": [
        "plt.plot(y_test)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f241e18e320>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FG0lNM2txiyL"
      },
      "source": [
        "# Saving the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8owfMUsPrCO1"
      },
      "source": [
        "model.save('model.h5')"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uPnWoVrvs-iR"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}